


Why Use AI?
The best decisions made in any business or organisation are those that are supported by analysis of the data that the company has at its disposal. An average business only ever analyses around 10% of its data, whether this be sales figures, customer
habits, demographic or geographic trends. This is usually because an entire organisation’s data is vast and the compute power and time to analyse more is too time consuming or costly; however the deeper you analyse the more insight you get
and the better decisions, changes and improvements you can make.
The rise of GPUs, delivering hugely parallel compute power, and the growth in available data has led to the ability to analyse the majority or even all of an organisation’s data - but crucially in much shorter timeframes than ever
before. The huge range of GPUs on the market now enable even a single workstation to add value by rapidly analysing datasets, and with larger budgets the sky's the limit when it comes to GPU-accelerated datacentres. This is why the use of
AI and deep learning technologies is in exponential growth within almost every business sector.
Case Studies

inooLabs
Using Natural Language Processing inooLabs has built a product which delivers information to students on any given topic from multiple sources in a simple manner.
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King’s College London
King’s College London is bringing artificial intelligence in medical imaging allowing the results gained from X-rays, CT or MRI scans to be delivered immediately at the time of the patient-doctor interaction.
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EnvirometriX and OpenGeoHub
Learn how OpenGeoHub and EnvirometriX pioneered a global predictive vegetation and soil mapping system using AI technology.
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FDL Europe 2020 - Clouds and Aerosols
The project team used observations from a geo-stationary satellite over the Southern Atlantic Ocean, combined with data from ECMWF and IMERG estimates to better understand the aerosol impact on cloud structure through the application of multiple machine learning methods.
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FDL Europe 2020 - Digital Twin Earth
The DTE project set out to discover whether machine learning can learn forecast precipitation by fusing simulated satellite weather data with physical model data, to offer a low-cost alternative to expensive simulation infrastructure.
Read moreAI in Research & Higher Education
Higher education is at the frontlines of major global challenges, training innovators in AI, accelerated computing, and data science. At the same time, institutions need to meet the demand for more flexible, accessible education options. The ability to handle large workloads, increase efficiency, and lower operational costs with centralised infrastructure and computational excellence is key and this can be delivered in any learning environment by using GPU-accelerated AI and high-performance computing (HPC), to enable researchers to leverage modelling, simulation, and experimental datasets to address even the biggest challenges.
Research
Today’s research requires infrastructure that can handle large computational workloads to derive fast and accurate insights from vast amounts of data. Deep learning systems lowers the cost of computing infrastructure and accelerates the performance of HPC and AI applications.
AI Training
Get hands-on training in AI, data science, and accelerated computing to solve real-world problems. Through online courses and instructor-led workshops students can learn the latest techniques for designing and deploying neural networks.
Pre-Trained AI models
Building AI models can be complex and time-consuming but NVIDIA GPU systems are supported by a hub of essential software for deep learning, machine learning, and HPC with pre-trained AI models, model training scripts, and industry-specific software stacks.
The Scan AI Ecosystem Explained
The Scan AI ecosystem has been created so you have a trusted partner at every stage of your AI journey - whether you’re in early development of a model, in the midst of repeated training cycles or working on a complete inference deployment. Our in-house AI experts, backed by our system design and build teams are able to offer bespoke solutions that solve your AI challenges. Furthermore, we are complimented by a whole host of hardware and software partners enabling us to deliver end-to-end AI optimised architectures configured to get the maximum return from your investment and the deepest insight from your data.

Guided proof of concept
Our proof of concept hardware environment ensures you choose the correct solution for your particular AI challenge. Housed within a number of secure data centres we operate a wide variety of GPU-accelerated AI systems. These range from development workstations, multi-GPU servers, NVIDIA DGX appliances, software-defined storage solutions and Advantech modular inferencing solutions. The ability to test a data set, check system compatibility or simply try-before-you-buy across such a varied degree of hardware and software solutions, we believe, is unique in the UK AI industry. Why not enquire today?
Learn MoreBeginner’s Guide to Deep Learning & AI
Now you know how AI and deep learning could help your organisation, why not read on to learn more about the process of deep learning, its terminology, how to prepare your data and where to get started.
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